{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scientist Nanodegree\n",
    "\n",
    "## Convolutional Neural Networks\n",
    "\n",
    "## Project: Write an Algorithm for a Dog Identification App \n",
    "\n",
    "\n",
    "This notebook walks you through one of the most popular Udacity projects across machine learning and artificial intellegence nanodegree programs.  The goal is to classify images of dogs according to their breed.  \n",
    "\n",
    "If you are looking for a more guided capstone project related to deep learning and convolutional neural networks, this might be just it.  Notice that even if you follow the notebook to creating your classifier, you must still create a blog post or deploy an application to fulfill the requirements of the capstone project.\n",
    "\n",
    "Also notice, you may be able to use only parts of this notebook (for example certain coding portions or the data) without completing all parts and still meet all requirements of the capstone project.\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! \n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.\n",
    "\n",
    "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this IPython notebook.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "### Why We're Here \n",
    "\n",
    "In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!). \n",
    "\n",
    "![Sample Dog Output](images/sample_dog_output.png)\n",
    "\n",
    "In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!\n",
    "\n",
    "### The Road Ahead\n",
    "\n",
    "We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.\n",
    "\n",
    "* [Step 0](#step0): Import Datasets\n",
    "* [Step 1](#step1): Detect Humans\n",
    "* [Step 2](#step2): Detect Dogs\n",
    "* [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "* [Step 4](#step4): Use a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 5](#step5): Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 6](#step6): Write your Algorithm\n",
    "* [Step 7](#step7): Test Your Algorithm\n",
    "\n",
    "---\n",
    "<a id='step0'></a>\n",
    "## Step 0: Import Datasets\n",
    "\n",
    "### Import Dog Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the `load_files` function from the scikit-learn library:\n",
    "- `train_files`, `valid_files`, `test_files` - numpy arrays containing file paths to images\n",
    "- `train_targets`, `valid_targets`, `test_targets` - numpy arrays containing onehot-encoded classification labels \n",
    "- `dog_names` - list of string-valued dog breed names for translating labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 133 total dog categories.\n",
      "There are 8351 total dog images.\n",
      "\n",
      "There are 6680 training dog images.\n",
      "There are 835 validation dog images.\n",
      "There are 836 test dog images.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# define function to load train, test, and validation datasets\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# load train, test, and validation datasets\n",
    "train_files, train_targets = load_dataset('../../../data/dog_images/train')\n",
    "valid_files, valid_targets = load_dataset('../../../data/dog_images/valid')\n",
    "test_files, test_targets = load_dataset('../../../data/dog_images/test')\n",
    "\n",
    "# load list of dog names\n",
    "dog_names = [item[20:-1] for item in sorted(glob(\"../../../data/dog_images/train/*/\"))]\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total dog categories.' % len(dog_names))\n",
    "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training dog images.' % len(train_files))\n",
    "print('There are %d validation dog images.' % len(valid_files))\n",
    "print('There are %d test dog images.'% len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Human Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array `human_files`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13233 total human images.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(8675309)\n",
    "\n",
    "# load filenames in shuffled human dataset\n",
    "human_files = np.array(glob(\"../../../data/lfw/*/*\"))\n",
    "random.shuffle(human_files)\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total human images.' % len(human_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## Step 1: Detect Humans\n",
    "\n",
    "We use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images.  OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades).  We have downloaded one of these detectors and stored it in the `haarcascades` directory.\n",
    "\n",
    "In the next code cell, we demonstrate how to use this detector to find human faces in a sample image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of faces detected: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c583e2b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2                \n",
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline                               \n",
    "\n",
    "# extract pre-trained face detector\n",
    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n",
    "\n",
    "# load color (BGR) image\n",
    "img = cv2.imread(human_files[3])\n",
    "# convert BGR image to grayscale\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# find faces in image\n",
    "faces = face_cascade.detectMultiScale(gray)\n",
    "\n",
    "# print number of faces detected in the image\n",
    "print('Number of faces detected:', len(faces))\n",
    "\n",
    "# get bounding box for each detected face\n",
    "for (x,y,w,h) in faces:\n",
    "    # add bounding box to color image\n",
    "    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "    \n",
    "# convert BGR image to RGB for plotting\n",
    "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# display the image, along with bounding box\n",
    "plt.imshow(cv_rgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter.  \n",
    "\n",
    "In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.\n",
    "\n",
    "### Write a Human Face Detector\n",
    "\n",
    "We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise.  This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns \"True\" if face is detected in image stored at img_path\n",
    "def face_detector(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray)\n",
    "    return len(faces) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Human Face Detector\n",
    "\n",
    "__Question 1:__ Use the code cell below to test the performance of the `face_detector` function.  \n",
    "- What percentage of the first 100 images in `human_files` have a detected human face?  \n",
    "- What percentage of the first 100 images in `dog_files` have a detected human face? \n",
    "\n",
    "Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "# Do NOT modify the code above this line.\n",
    "\n",
    "## TODO: Test the performance of the face_detector algorithm \n",
    "## on the images in human_files_short and dog_files_short."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_human(file_array):\n",
    "    is_human_face = [ face_detector(file) for file in file_array ]\n",
    "    return np.mean(is_human_face)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_human(human_files_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_human(dog_files_short)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?\n",
    "\n",
    "__Answer:__\n",
    "\n",
    "We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this _optional_ task, report performance on each of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## (Optional) TODO: Report the performance of another  \n",
    "## face detection algorithm on the LFW dataset\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "## Step 2: Detect Dogs\n",
    "\n",
    "In this section, we use a pre-trained [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a).  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
      "102858752/102853048 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "\n",
    "# define ResNet50 model\n",
    "ResNet50_model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data\n",
    "\n",
    "When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape\n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, \\text{rows}, \\text{columns}, \\text{channels}),\n",
    "$$\n",
    "\n",
    "where `nb_samples` corresponds to the total number of images (or samples), and `rows`, `columns`, and `channels` correspond to the number of rows, columns, and channels for each image, respectively.  \n",
    "\n",
    "The `path_to_tensor` function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \\times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape\n",
    "\n",
    "$$\n",
    "(1, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "The `paths_to_tensor` function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape \n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "Here, `nb_samples` is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of `nb_samples` as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions with ResNet-50\n",
    "\n",
    "Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing.  First, the RGB image is converted to BGR by reordering the channels.  All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image.  This is implemented in the imported function `preprocess_input`.  If you're curious, you can check the code for `preprocess_input` [here](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py).\n",
    "\n",
    "Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions.  This is accomplished with the `predict` method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category.  This is implemented in the `ResNet50_predict_labels` function below.\n",
    "\n",
    "By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "def ResNet50_predict_labels(img_path):\n",
    "    # returns prediction vector for image located at img_path\n",
    "    img = preprocess_input(path_to_tensor(img_path))\n",
    "    return np.argmax(ResNet50_model.predict(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Dog Detector\n",
    "\n",
    "While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the `ResNet50_predict_labels` function above returns a value between 151 and 268 (inclusive).\n",
    "\n",
    "We use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### returns \"True\" if a dog is detected in the image stored at img_path\n",
    "def dog_detector(img_path):\n",
    "    prediction = ResNet50_predict_labels(img_path)\n",
    "    return ((prediction <= 268) & (prediction >= 151)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Dog Detector\n",
    "\n",
    "__Question 3:__ Use the code cell below to test the performance of your `dog_detector` function.  \n",
    "- What percentage of the images in `human_files_short` have a detected dog?  \n",
    "- What percentage of the images in `dog_files_short` have a detected dog?\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_dog(file_array):\n",
    "    is_dog = [ dog_detector(file) for file in file_array ]\n",
    "    return np.mean(is_dog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_dog(human_files_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_dog(dog_files_short)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step3'></a>\n",
    "## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "\n",
    "Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.\n",
    "\n",
    "Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train. \n",
    "\n",
    "We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that *even a human* would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.  \n",
    "\n",
    "Brittany | Welsh Springer Spaniel\n",
    "- | - \n",
    "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
    "\n",
    "It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).  \n",
    "\n",
    "Curly-Coated Retriever | American Water Spaniel\n",
    "- | -\n",
    "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n",
    "\n",
    "\n",
    "Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.  \n",
    "\n",
    "Yellow Labrador | Chocolate Labrador | Black Labrador\n",
    "- | -\n",
    "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n",
    "\n",
    "We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.  \n",
    "\n",
    "Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun! \n",
    "\n",
    "### Pre-process the Data\n",
    "\n",
    "We rescale the images by dividing every pixel in every image by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6680/6680 [01:13<00:00, 90.37it/s] \n",
      "100%|██████████| 835/835 [00:08<00:00, 100.22it/s]\n",
      "100%|██████████| 836/836 [00:08<00:00, 101.12it/s]\n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFile\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
    "\n",
    "# pre-process the data for Keras\n",
    "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n",
    "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n",
    "test_tensors = paths_to_tensor(test_files).astype('float32')/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        model.summary()\n",
    "\n",
    "We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:\n",
    "\n",
    "![Sample CNN](images/sample_cnn.png)\n",
    "           \n",
    "__Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 111, 111, 32)      2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 55, 55, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 54, 54, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 27, 27, 64)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 133)               8645      \n",
      "=================================================================\n",
      "Total params: 19,189\n",
      "Trainable params: 19,189\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "### TODO: Define your architecture.\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', \n",
    "                        input_shape=(224, 224, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dense(len(dog_names), activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.\n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.8846 - acc: 0.0085 - val_loss: 4.8687 - val_acc: 0.0132\n",
      "Epoch 2/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.8536 - acc: 0.0136 - val_loss: 4.8297 - val_acc: 0.0216\n",
      "Epoch 3/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.8092 - acc: 0.0193 - val_loss: 4.8002 - val_acc: 0.0168\n",
      "Epoch 4/20\n",
      "6680/6680 [==============================] - 20s 3ms/step - loss: 4.7765 - acc: 0.0190 - val_loss: 4.7778 - val_acc: 0.0204\n",
      "Epoch 5/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.7495 - acc: 0.0220 - val_loss: 4.7538 - val_acc: 0.0216\n",
      "Epoch 6/20\n",
      "6680/6680 [==============================] - 20s 3ms/step - loss: 4.7214 - acc: 0.0243 - val_loss: 4.7362 - val_acc: 0.0228\n",
      "Epoch 7/20\n",
      "6680/6680 [==============================] - 20s 3ms/step - loss: 4.6971 - acc: 0.0241 - val_loss: 4.7278 - val_acc: 0.0216\n",
      "Epoch 8/20\n",
      "6680/6680 [==============================] - 20s 3ms/step - loss: 4.6798 - acc: 0.0269 - val_loss: 4.7179 - val_acc: 0.0216\n",
      "Epoch 9/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.6611 - acc: 0.0281 - val_loss: 4.7225 - val_acc: 0.0240\n",
      "Epoch 10/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.6444 - acc: 0.0319 - val_loss: 4.7328 - val_acc: 0.0335\n",
      "Epoch 11/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.6259 - acc: 0.0370 - val_loss: 4.6997 - val_acc: 0.0275\n",
      "Epoch 12/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.6048 - acc: 0.0400 - val_loss: 4.6788 - val_acc: 0.0287\n",
      "Epoch 13/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5894 - acc: 0.0418 - val_loss: 4.6503 - val_acc: 0.0347\n",
      "Epoch 14/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5742 - acc: 0.0454 - val_loss: 4.6616 - val_acc: 0.0383\n",
      "Epoch 15/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5555 - acc: 0.0451 - val_loss: 4.6286 - val_acc: 0.0443\n",
      "Epoch 16/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5423 - acc: 0.0445 - val_loss: 4.6528 - val_acc: 0.0383\n",
      "Epoch 17/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5202 - acc: 0.0525 - val_loss: 4.6063 - val_acc: 0.0371\n",
      "Epoch 18/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.5056 - acc: 0.0524 - val_loss: 4.6156 - val_acc: 0.0419\n",
      "Epoch 19/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.4901 - acc: 0.0534 - val_loss: 4.6330 - val_acc: 0.0335\n",
      "Epoch 20/20\n",
      "6680/6680 [==============================] - 21s 3ms/step - loss: 4.4738 - acc: 0.0572 - val_loss: 4.5588 - val_acc: 0.0431\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd2424788d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint \n",
    "\n",
    "filepath_model = 'saved_models/weights.best.from_scratch.hdf5'\n",
    "train = train_tensors\n",
    "valid = valid_tensors\n",
    "batch_size = 20\n",
    "epochs = 20\n",
    "checkpointer = ModelCheckpoint(filepath=filepath_model, save_best_only=True)\n",
    "\n",
    "model.fit(\n",
    "        train, train_targets, \n",
    "        validation_data=(valid, valid_targets),\n",
    "        epochs=epochs, batch_size=batch_size, callbacks=[checkpointer]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('saved_models/weights.best.from_scratch.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 5.3828%\n"
     ]
    }
   ],
   "source": [
    "# get index of predicted dog breed for each image in test set\n",
    "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "# report test accuracy\n",
    "test_accuracy = 100 * np.sum(np.array(dog_breed_predictions) == np.argmax(test_targets, axis=1)) / len(dog_breed_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step4'></a>\n",
    "## Step 4: Use a CNN to Classify Dog Breeds\n",
    "\n",
    "To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.\n",
    "\n",
    "### Instructor's Notes\n",
    "\n",
    "For these upcoming transfer learning exercises, I've refactored the code to make it more usable and easier to follow, as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# define function to load train, test, and validation datasets\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# load train, test, and validation datasets\n",
    "train_files, train_targets = load_dataset('../../../data/dog_images/train')\n",
    "valid_files, valid_targets = load_dataset('../../../data/dog_images/valid')\n",
    "test_files, test_targets = load_dataset('../../../data/dog_images/test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain Bottleneck Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture\n",
    "\n",
    "The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_GAP_layer(filepath_bottleneck, num_categories=133):\n",
    "    \"\"\"\n",
    "    Build layers on top of a CNN model\n",
    "    \n",
    "    Inputs:\n",
    "        filepath_bottleneck - filepath where the bottleneck file is saved\n",
    "        num_categories = number of categories to fit into the CNN\n",
    "    \n",
    "    Return:\n",
    "        model - built Keras CNN model\n",
    "    \n",
    "    \"\"\"\n",
    "    from keras.layers import Dense, GlobalAveragePooling2D\n",
    "    from keras.models import Sequential\n",
    "    import numpy as np\n",
    "    \n",
    "    # 1. get input shape\n",
    "    bottleneck_features = np.load(filepath_bottleneck)\n",
    "    train = bottleneck_features['train']\n",
    "    \n",
    "    # 2. add layers to model\n",
    "    model = Sequential()\n",
    "    model.add(GlobalAveragePooling2D(input_shape=train.shape[1:]))\n",
    "    model.add(Dense(num_categories, activation='softmax'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def finetune_model(model, filepath_bottleneck, filepath_model, batch_size=20, epochs=20, plot_loss=True):\n",
    "    \"\"\"\n",
    "    Perform transfer learning by extracting the bottleneck features and\n",
    "    overlaying it with a model architecture for learning 133 dog breeds\n",
    "    \n",
    "    Note that this requires a model with built layers in order for this\n",
    "    function to work\n",
    "    \n",
    "    Inputs:\n",
    "        model - a keras.models model, e.g. Sequential\n",
    "        filepath_bottleneck - filepath where the bottleneck file is saved\n",
    "        filepath_model - filepath to save and load the best model\n",
    "        batch_size - alpha (if too large, loss will diverge)\n",
    "        epochs - number of iterations\n",
    "        plot_loss - check if the model is under/overfitting\n",
    "    \n",
    "    Return:\n",
    "        none\n",
    "        \n",
    "    \"\"\"\n",
    "    from keras.callbacks import ModelCheckpoint    \n",
    "    import matplotlib.pyplot as plt\n",
    "    import pandas as pd\n",
    "    %matplotlib inline\n",
    "    \n",
    "    # 1. extract bottleneck features\n",
    "    bottleneck_features = np.load(filepath_bottleneck)\n",
    "    train = bottleneck_features['train']\n",
    "    valid = bottleneck_features['valid']\n",
    "    test = bottleneck_features['test']\n",
    "    \n",
    "    # 2. compile model\n",
    "    model.compile(\n",
    "        loss='categorical_crossentropy', \n",
    "        optimizer='rmsprop', \n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    \n",
    "    # 3. train and save the best model\n",
    "    checkpointer = ModelCheckpoint(filepath=filepath_model, save_best_only=True)\n",
    "    history = model.fit(\n",
    "        train, train_targets, \n",
    "        validation_data=(valid, valid_targets),\n",
    "        epochs=epochs, batch_size=batch_size, callbacks=[checkpointer]\n",
    "    )\n",
    "    \n",
    "    # 4. plot loss function for train and validation sets\n",
    "    if plot_loss:\n",
    "        hist_df = pd.DataFrame(history.history) \n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(\n",
    "            hist_df.index.values, \n",
    "            hist_df.val_loss.values, \n",
    "            c='m', marker=\"o\", ls='--', \n",
    "            label='Validation Set', fillstyle='none'\n",
    "        )\n",
    "        ax.plot(\n",
    "            hist_df.index.values, \n",
    "            hist_df.loss.values, \n",
    "            c='k', marker=\"+\", ls=':', \n",
    "            label='Training Set'\n",
    "        )\n",
    "        plt.legend(loc=2)\n",
    "        plt.show()\n",
    "    \n",
    "    # 5. print test accuracy of predicted dog breed\n",
    "    predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0))) for feature in test]\n",
    "    test_accuracy = np.sum(\n",
    "        np.array(predictions) == np.argmax(test_targets, axis=1)\n",
    "    ) * 100 / len(predictions)\n",
    "    print('Test accuracy: %.4f%%' % test_accuracy)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/100\n",
      "6680/6680 [==============================] - 1s 196us/step - loss: 12.2999 - acc: 0.0969 - val_loss: 10.3753 - val_acc: 0.1665\n",
      "Epoch 2/100\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 9.2010 - acc: 0.2790 - val_loss: 9.1089 - val_acc: 0.2826\n",
      "Epoch 3/100\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 8.2158 - acc: 0.3859 - val_loss: 8.5011 - val_acc: 0.3317\n",
      "Epoch 4/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 7.7685 - acc: 0.4427 - val_loss: 8.2507 - val_acc: 0.3617\n",
      "Epoch 5/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 7.5498 - acc: 0.4754 - val_loss: 8.1172 - val_acc: 0.3725\n",
      "Epoch 6/100\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 7.4081 - acc: 0.4966 - val_loss: 8.0308 - val_acc: 0.3976\n",
      "Epoch 7/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 7.2983 - acc: 0.5166 - val_loss: 7.9698 - val_acc: 0.4120\n",
      "Epoch 8/100\n",
      "6680/6680 [==============================] - 1s 142us/step - loss: 7.2469 - acc: 0.5295 - val_loss: 7.9236 - val_acc: 0.4108\n",
      "Epoch 9/100\n",
      "6680/6680 [==============================] - 1s 159us/step - loss: 7.2095 - acc: 0.5383 - val_loss: 7.9068 - val_acc: 0.4168\n",
      "Epoch 10/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 7.1922 - acc: 0.5421 - val_loss: 7.8701 - val_acc: 0.4299\n",
      "Epoch 11/100\n",
      "6680/6680 [==============================] - 1s 146us/step - loss: 7.0865 - acc: 0.5464 - val_loss: 7.7278 - val_acc: 0.4204\n",
      "Epoch 12/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 6.8944 - acc: 0.5587 - val_loss: 7.5730 - val_acc: 0.4515\n",
      "Epoch 13/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 6.8521 - acc: 0.5657 - val_loss: 7.5551 - val_acc: 0.4347\n",
      "Epoch 14/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.7945 - acc: 0.5683 - val_loss: 7.5570 - val_acc: 0.4395\n",
      "Epoch 15/100\n",
      "6680/6680 [==============================] - 1s 145us/step - loss: 6.7747 - acc: 0.5725 - val_loss: 7.5456 - val_acc: 0.4431\n",
      "Epoch 16/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.7603 - acc: 0.5765 - val_loss: 7.5243 - val_acc: 0.4551\n",
      "Epoch 17/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 6.7586 - acc: 0.5772 - val_loss: 7.4890 - val_acc: 0.4647\n",
      "Epoch 18/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 6.7542 - acc: 0.5780 - val_loss: 7.5168 - val_acc: 0.4479\n",
      "Epoch 19/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 6.7546 - acc: 0.5786 - val_loss: 7.4328 - val_acc: 0.4575\n",
      "Epoch 20/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 6.7501 - acc: 0.5792 - val_loss: 7.4982 - val_acc: 0.4551\n",
      "Epoch 21/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 6.7411 - acc: 0.5789 - val_loss: 7.5770 - val_acc: 0.4431\n",
      "Epoch 22/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 6.6833 - acc: 0.5789 - val_loss: 7.4928 - val_acc: 0.4539\n",
      "Epoch 23/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 6.6280 - acc: 0.5820 - val_loss: 7.4504 - val_acc: 0.4503\n",
      "Epoch 24/100\n",
      "6680/6680 [==============================] - 1s 156us/step - loss: 6.5804 - acc: 0.5858 - val_loss: 7.5264 - val_acc: 0.4479\n",
      "Epoch 25/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 6.5094 - acc: 0.5877 - val_loss: 7.3147 - val_acc: 0.4539\n",
      "Epoch 26/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 6.4152 - acc: 0.5949 - val_loss: 7.2437 - val_acc: 0.4599\n",
      "Epoch 27/100\n",
      "6680/6680 [==============================] - 1s 152us/step - loss: 6.3911 - acc: 0.5993 - val_loss: 7.2610 - val_acc: 0.4683\n",
      "Epoch 28/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.3797 - acc: 0.6006 - val_loss: 7.3005 - val_acc: 0.4695\n",
      "Epoch 29/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.3738 - acc: 0.6007 - val_loss: 7.2808 - val_acc: 0.4635\n",
      "Epoch 30/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 6.2702 - acc: 0.6060 - val_loss: 7.1617 - val_acc: 0.4671\n",
      "Epoch 31/100\n",
      "6680/6680 [==============================] - 1s 150us/step - loss: 6.2484 - acc: 0.6100 - val_loss: 7.1350 - val_acc: 0.4754\n",
      "Epoch 32/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.2442 - acc: 0.6102 - val_loss: 7.2063 - val_acc: 0.4802\n",
      "Epoch 33/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 6.2399 - acc: 0.6117 - val_loss: 7.1766 - val_acc: 0.4826\n",
      "Epoch 34/100\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 6.2401 - acc: 0.6115 - val_loss: 7.1728 - val_acc: 0.4802\n",
      "Epoch 35/100\n",
      "6680/6680 [==============================] - 1s 151us/step - loss: 6.2363 - acc: 0.6121 - val_loss: 7.1817 - val_acc: 0.4814\n",
      "Epoch 36/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.2369 - acc: 0.6124 - val_loss: 7.1852 - val_acc: 0.4826\n",
      "Epoch 37/100\n",
      "6680/6680 [==============================] - 1s 144us/step - loss: 6.2358 - acc: 0.6115 - val_loss: 7.2263 - val_acc: 0.4886\n",
      "Epoch 38/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 6.2344 - acc: 0.6117 - val_loss: 7.1815 - val_acc: 0.4862\n",
      "Epoch 39/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 6.2212 - acc: 0.6112 - val_loss: 7.2254 - val_acc: 0.4778\n",
      "Epoch 40/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 6.1567 - acc: 0.6117 - val_loss: 7.1165 - val_acc: 0.4850\n",
      "Epoch 41/100\n",
      "6680/6680 [==============================] - 1s 147us/step - loss: 6.1110 - acc: 0.6168 - val_loss: 7.1173 - val_acc: 0.4802\n",
      "Epoch 42/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 6.0702 - acc: 0.6177 - val_loss: 7.1712 - val_acc: 0.4719\n",
      "Epoch 43/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 6.0014 - acc: 0.6202 - val_loss: 6.9903 - val_acc: 0.4850\n",
      "Epoch 44/100\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 5.9363 - acc: 0.6247 - val_loss: 7.0152 - val_acc: 0.4898\n",
      "Epoch 45/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 5.9057 - acc: 0.6295 - val_loss: 7.0098 - val_acc: 0.4886\n",
      "Epoch 46/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 5.8972 - acc: 0.6313 - val_loss: 6.9977 - val_acc: 0.4994\n",
      "Epoch 47/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.8884 - acc: 0.6314 - val_loss: 7.0418 - val_acc: 0.4850\n",
      "Epoch 48/100\n",
      "6680/6680 [==============================] - 1s 146us/step - loss: 5.8448 - acc: 0.6322 - val_loss: 6.9965 - val_acc: 0.4958\n",
      "Epoch 49/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.8046 - acc: 0.6371 - val_loss: 6.9279 - val_acc: 0.5018\n",
      "Epoch 50/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 5.7997 - acc: 0.6379 - val_loss: 6.9746 - val_acc: 0.4934\n",
      "Epoch 51/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 5.7983 - acc: 0.6386 - val_loss: 6.8629 - val_acc: 0.5066\n",
      "Epoch 52/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 5.7732 - acc: 0.6388 - val_loss: 6.9833 - val_acc: 0.4838\n",
      "Epoch 53/100\n",
      "6680/6680 [==============================] - 1s 147us/step - loss: 5.6769 - acc: 0.6430 - val_loss: 6.8443 - val_acc: 0.4982\n",
      "Epoch 54/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 5.5365 - acc: 0.6487 - val_loss: 6.7236 - val_acc: 0.4922\n",
      "Epoch 55/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 5.4803 - acc: 0.6546 - val_loss: 6.7299 - val_acc: 0.5030\n",
      "Epoch 56/100\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 5.4678 - acc: 0.6578 - val_loss: 6.6030 - val_acc: 0.5138\n",
      "Epoch 57/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 5.4625 - acc: 0.6588 - val_loss: 6.6436 - val_acc: 0.5150\n",
      "Epoch 58/100\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 5.4613 - acc: 0.6591 - val_loss: 6.5694 - val_acc: 0.5234\n",
      "Epoch 59/100\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 5.4603 - acc: 0.6585 - val_loss: 6.6129 - val_acc: 0.5150\n",
      "Epoch 60/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 5.4576 - acc: 0.6596 - val_loss: 6.6167 - val_acc: 0.5174\n",
      "Epoch 61/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.4490 - acc: 0.6597 - val_loss: 6.6780 - val_acc: 0.5114\n",
      "Epoch 62/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.4174 - acc: 0.6596 - val_loss: 6.6792 - val_acc: 0.4994\n",
      "Epoch 63/100\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 5.3952 - acc: 0.6618 - val_loss: 6.6489 - val_acc: 0.5162\n",
      "Epoch 64/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.3880 - acc: 0.6633 - val_loss: 6.6636 - val_acc: 0.5066\n",
      "Epoch 65/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 5.3816 - acc: 0.6642 - val_loss: 6.6406 - val_acc: 0.5150\n",
      "Epoch 66/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 5.3798 - acc: 0.6653 - val_loss: 6.6213 - val_acc: 0.5114\n",
      "Epoch 67/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 5.3784 - acc: 0.6645 - val_loss: 6.6214 - val_acc: 0.5329\n",
      "Epoch 68/100\n",
      "6680/6680 [==============================] - 1s 153us/step - loss: 5.3471 - acc: 0.6633 - val_loss: 6.6491 - val_acc: 0.5042\n",
      "Epoch 69/100\n",
      "6680/6680 [==============================] - 1s 159us/step - loss: 5.2370 - acc: 0.6690 - val_loss: 6.5550 - val_acc: 0.5066\n",
      "Epoch 70/100\n",
      "6680/6680 [==============================] - 1s 161us/step - loss: 5.1954 - acc: 0.6731 - val_loss: 6.4675 - val_acc: 0.5102\n",
      "Epoch 71/100\n",
      "6680/6680 [==============================] - 1s 149us/step - loss: 5.1762 - acc: 0.6746 - val_loss: 6.5197 - val_acc: 0.5162\n",
      "Epoch 72/100\n",
      "6680/6680 [==============================] - 1s 147us/step - loss: 5.1654 - acc: 0.6765 - val_loss: 6.5232 - val_acc: 0.5210\n",
      "Epoch 73/100\n",
      "6680/6680 [==============================] - 1s 148us/step - loss: 5.1622 - acc: 0.6774 - val_loss: 6.4862 - val_acc: 0.5114\n",
      "Epoch 74/100\n",
      "6680/6680 [==============================] - 1s 157us/step - loss: 5.1571 - acc: 0.6780 - val_loss: 6.5121 - val_acc: 0.5186\n",
      "Epoch 75/100\n",
      "6680/6680 [==============================] - 1s 147us/step - loss: 5.1552 - acc: 0.6793 - val_loss: 6.4471 - val_acc: 0.5210\n",
      "Epoch 76/100\n",
      "6680/6680 [==============================] - 1s 148us/step - loss: 5.1532 - acc: 0.6795 - val_loss: 6.5585 - val_acc: 0.5198\n",
      "Epoch 77/100\n",
      "6680/6680 [==============================] - 1s 148us/step - loss: 5.1557 - acc: 0.6790 - val_loss: 6.4197 - val_acc: 0.5222\n",
      "Epoch 78/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.1543 - acc: 0.6795 - val_loss: 6.4451 - val_acc: 0.5246\n",
      "Epoch 79/100\n",
      "6680/6680 [==============================] - 1s 148us/step - loss: 5.1534 - acc: 0.6793 - val_loss: 6.4657 - val_acc: 0.5246\n",
      "Epoch 80/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.1537 - acc: 0.6796 - val_loss: 6.4849 - val_acc: 0.5210\n",
      "Epoch 81/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.1544 - acc: 0.6796 - val_loss: 6.4350 - val_acc: 0.5293\n",
      "Epoch 82/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.1530 - acc: 0.6796 - val_loss: 6.5013 - val_acc: 0.5210\n",
      "Epoch 83/100\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 5.1542 - acc: 0.6796 - val_loss: 6.5182 - val_acc: 0.5234\n",
      "Epoch 84/100\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 5.1534 - acc: 0.6798 - val_loss: 6.4888 - val_acc: 0.5222\n",
      "Epoch 85/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 5.1517 - acc: 0.6798 - val_loss: 6.5267 - val_acc: 0.5210\n",
      "Epoch 86/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 5.1539 - acc: 0.6796 - val_loss: 6.4786 - val_acc: 0.5186\n",
      "Epoch 87/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.1530 - acc: 0.6796 - val_loss: 6.4531 - val_acc: 0.5317\n",
      "Epoch 88/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 5.1539 - acc: 0.6796 - val_loss: 6.4082 - val_acc: 0.5317\n",
      "Epoch 89/100\n",
      "6680/6680 [==============================] - 1s 152us/step - loss: 5.1545 - acc: 0.6795 - val_loss: 6.4574 - val_acc: 0.5257\n",
      "Epoch 90/100\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 5.1529 - acc: 0.6795 - val_loss: 6.4824 - val_acc: 0.5293\n",
      "Epoch 91/100\n",
      "6680/6680 [==============================] - 1s 150us/step - loss: 5.1504 - acc: 0.6795 - val_loss: 6.5193 - val_acc: 0.5234\n",
      "Epoch 92/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.1427 - acc: 0.6790 - val_loss: 6.4948 - val_acc: 0.5293\n",
      "Epoch 93/100\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 5.1317 - acc: 0.6792 - val_loss: 6.4781 - val_acc: 0.5329\n",
      "Epoch 94/100\n",
      "6680/6680 [==============================] - 1s 149us/step - loss: 5.1020 - acc: 0.6807 - val_loss: 6.4417 - val_acc: 0.5377\n",
      "Epoch 95/100\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 5.0739 - acc: 0.6825 - val_loss: 6.4024 - val_acc: 0.5269\n",
      "Epoch 96/100\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 5.0662 - acc: 0.6834 - val_loss: 6.4138 - val_acc: 0.5305\n",
      "Epoch 97/100\n",
      "6680/6680 [==============================] - 1s 145us/step - loss: 5.0610 - acc: 0.6849 - val_loss: 6.4470 - val_acc: 0.5341\n",
      "Epoch 98/100\n",
      "6680/6680 [==============================] - 1s 137us/step - loss: 5.0613 - acc: 0.6855 - val_loss: 6.4191 - val_acc: 0.5305\n",
      "Epoch 99/100\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 5.0591 - acc: 0.6855 - val_loss: 6.4340 - val_acc: 0.5257\n",
      "Epoch 100/100\n",
      "6680/6680 [==============================] - 1s 150us/step - loss: 5.0603 - acc: 0.6855 - val_loss: 6.4283 - val_acc: 0.5377\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd24233e780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 55.0239%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x7fd2423f1390>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogVGG16Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.VGG16.hdf5'\n",
    "VGG16_model = add_GAP_layer(filepath_bottleneck)\n",
    "finetune_model(VGG16_model, filepath_bottleneck, filepath_model, batch_size=50, epochs=100, plot_loss=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Dog Breed with the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogVGG16Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.VGG16.hdf5'\n",
    "VGG16_model = add_GAP_layer(filepath_bottleneck)\n",
    "VGG16_model.load_weights(filepath_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def VGG16_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n",
    "    # obtain predicted vector\n",
    "    predicted_vector = VGG16_model.predict(bottleneck_feature)\n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "## Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "\n",
    "You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.\n",
    "\n",
    "In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:\n",
    "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n",
    "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n",
    "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n",
    "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n",
    "\n",
    "The files are encoded as such:\n",
    "\n",
    "    Dog{network}Data.npz\n",
    "    \n",
    "where `{network}`, in the above filename, can be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`.  Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the `bottleneck_features/` folder in the repository.\n",
    "\n",
    "### (IMPLEMENTATION) Obtain Bottleneck Features\n",
    "\n",
    "In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:\n",
    "\n",
    "    bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')\n",
    "    train_{network} = bottleneck_features['train']\n",
    "    valid_{network} = bottleneck_features['valid']\n",
    "    test_{network} = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        <your model's name>.summary()\n",
    "   \n",
    "__Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.\n",
    "\n",
    "__Answer:__ \n",
    "\n",
    "For the below architecture, I chose to expand on Resnet50 since it had a better test set performance than that of VGG16.\n",
    "\n",
    "From there, I froze all the convolution layers and only added layers on top. I tried two architectures:\n",
    "1. Global Average Pooling; and\n",
    "2. Fully Connected Layers.\n",
    "\n",
    "For Global Average Pooling GAP), it's often used to replace fully connected layers in a classifier. This reduces overfitting by taking the average of all values in a filter. Then, it's fed into a Softmax activation function to get a multiclass probability distribution (i.e. the 133 dog breeds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Compile the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.  \n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/60\n",
      "6680/6680 [==============================] - 2s 231us/step - loss: 1.9467 - acc: 0.5539 - val_loss: 0.9872 - val_acc: 0.7114\n",
      "Epoch 2/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.5074 - acc: 0.8548 - val_loss: 0.7019 - val_acc: 0.7701\n",
      "Epoch 3/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.2834 - acc: 0.9189 - val_loss: 0.6225 - val_acc: 0.8036\n",
      "Epoch 4/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.1702 - acc: 0.9566 - val_loss: 0.6048 - val_acc: 0.8144\n",
      "Epoch 5/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.1138 - acc: 0.9735 - val_loss: 0.6304 - val_acc: 0.8156\n",
      "Epoch 6/60\n",
      "6680/6680 [==============================] - 1s 105us/step - loss: 0.0763 - acc: 0.9831 - val_loss: 0.6098 - val_acc: 0.7988\n",
      "Epoch 7/60\n",
      "6680/6680 [==============================] - 1s 105us/step - loss: 0.0531 - acc: 0.9898 - val_loss: 0.6311 - val_acc: 0.8060\n",
      "Epoch 8/60\n",
      "6680/6680 [==============================] - 1s 103us/step - loss: 0.0359 - acc: 0.9930 - val_loss: 0.6226 - val_acc: 0.8108\n",
      "Epoch 9/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0264 - acc: 0.9955 - val_loss: 0.6952 - val_acc: 0.8000\n",
      "Epoch 10/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0200 - acc: 0.9964 - val_loss: 0.7244 - val_acc: 0.8000\n",
      "Epoch 11/60\n",
      "6680/6680 [==============================] - 1s 103us/step - loss: 0.0155 - acc: 0.9973 - val_loss: 0.6934 - val_acc: 0.8168\n",
      "Epoch 12/60\n",
      "6680/6680 [==============================] - 1s 104us/step - loss: 0.0119 - acc: 0.9978 - val_loss: 0.7218 - val_acc: 0.8192\n",
      "Epoch 13/60\n",
      "6680/6680 [==============================] - 1s 114us/step - loss: 0.0099 - acc: 0.9976 - val_loss: 0.6910 - val_acc: 0.8240\n",
      "Epoch 14/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0079 - acc: 0.9982 - val_loss: 0.6970 - val_acc: 0.8275\n",
      "Epoch 15/60\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.0074 - acc: 0.9985 - val_loss: 0.7298 - val_acc: 0.8335\n",
      "Epoch 16/60\n",
      "6680/6680 [==============================] - 1s 113us/step - loss: 0.0059 - acc: 0.9988 - val_loss: 0.7758 - val_acc: 0.8263\n",
      "Epoch 17/60\n",
      "6680/6680 [==============================] - 1s 111us/step - loss: 0.0046 - acc: 0.9988 - val_loss: 0.7659 - val_acc: 0.8263\n",
      "Epoch 18/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0052 - acc: 0.9985 - val_loss: 0.7650 - val_acc: 0.8311\n",
      "Epoch 19/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0043 - acc: 0.9988 - val_loss: 0.8098 - val_acc: 0.8371\n",
      "Epoch 20/60\n",
      "6680/6680 [==============================] - 1s 126us/step - loss: 0.0047 - acc: 0.9985 - val_loss: 0.8156 - val_acc: 0.8287\n",
      "Epoch 21/60\n",
      "6680/6680 [==============================] - 1s 115us/step - loss: 0.0048 - acc: 0.9981 - val_loss: 0.8281 - val_acc: 0.8251\n",
      "Epoch 22/60\n",
      "6680/6680 [==============================] - 1s 116us/step - loss: 0.0048 - acc: 0.9990 - val_loss: 0.8565 - val_acc: 0.8323\n",
      "Epoch 23/60\n",
      "6680/6680 [==============================] - 1s 116us/step - loss: 0.0045 - acc: 0.9985 - val_loss: 0.8937 - val_acc: 0.8216\n",
      "Epoch 24/60\n",
      "6680/6680 [==============================] - 1s 115us/step - loss: 0.0042 - acc: 0.9985 - val_loss: 0.9012 - val_acc: 0.8287\n",
      "Epoch 25/60\n",
      "6680/6680 [==============================] - 1s 115us/step - loss: 0.0035 - acc: 0.9984 - val_loss: 0.9145 - val_acc: 0.8216\n",
      "Epoch 26/60\n",
      "6680/6680 [==============================] - 1s 117us/step - loss: 0.0045 - acc: 0.9987 - val_loss: 0.9242 - val_acc: 0.8216\n",
      "Epoch 27/60\n",
      "6680/6680 [==============================] - 1s 120us/step - loss: 0.0045 - acc: 0.9988 - val_loss: 0.9226 - val_acc: 0.8299\n",
      "Epoch 28/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0041 - acc: 0.9987 - val_loss: 0.9859 - val_acc: 0.8216\n",
      "Epoch 29/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0042 - acc: 0.9984 - val_loss: 0.9869 - val_acc: 0.8216\n",
      "Epoch 30/60\n",
      "6680/6680 [==============================] - 1s 120us/step - loss: 0.0044 - acc: 0.9990 - val_loss: 0.9684 - val_acc: 0.8204\n",
      "Epoch 31/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0041 - acc: 0.9990 - val_loss: 1.0087 - val_acc: 0.8192\n",
      "Epoch 32/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0044 - acc: 0.9988 - val_loss: 1.0505 - val_acc: 0.8228\n",
      "Epoch 33/60\n",
      "6680/6680 [==============================] - 1s 114us/step - loss: 0.0043 - acc: 0.9988 - val_loss: 1.0007 - val_acc: 0.8299\n",
      "Epoch 34/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0037 - acc: 0.9987 - val_loss: 1.0302 - val_acc: 0.8263\n",
      "Epoch 35/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0038 - acc: 0.9988 - val_loss: 1.0116 - val_acc: 0.8359\n",
      "Epoch 36/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0044 - acc: 0.9987 - val_loss: 1.0207 - val_acc: 0.8299\n",
      "Epoch 37/60\n",
      "6680/6680 [==============================] - 1s 116us/step - loss: 0.0041 - acc: 0.9987 - val_loss: 1.0374 - val_acc: 0.8275\n",
      "Epoch 38/60\n",
      "6680/6680 [==============================] - 1s 105us/step - loss: 0.0041 - acc: 0.9988 - val_loss: 1.0588 - val_acc: 0.8299\n",
      "Epoch 39/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0040 - acc: 0.9985 - val_loss: 1.0947 - val_acc: 0.8263\n",
      "Epoch 40/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0039 - acc: 0.9991 - val_loss: 1.0734 - val_acc: 0.8299\n",
      "Epoch 41/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0045 - acc: 0.9985 - val_loss: 1.0424 - val_acc: 0.8371\n",
      "Epoch 42/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0041 - acc: 0.9988 - val_loss: 1.0668 - val_acc: 0.8347\n",
      "Epoch 43/60\n",
      "6680/6680 [==============================] - 1s 109us/step - loss: 0.0050 - acc: 0.9988 - val_loss: 1.0835 - val_acc: 0.8311\n",
      "Epoch 44/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0046 - acc: 0.9984 - val_loss: 1.0892 - val_acc: 0.8287\n",
      "Epoch 45/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0044 - acc: 0.9987 - val_loss: 1.0833 - val_acc: 0.8323\n",
      "Epoch 46/60\n",
      "6680/6680 [==============================] - 1s 104us/step - loss: 0.0053 - acc: 0.9987 - val_loss: 1.0866 - val_acc: 0.8228\n",
      "Epoch 47/60\n",
      "6680/6680 [==============================] - 1s 113us/step - loss: 0.0046 - acc: 0.9985 - val_loss: 1.0951 - val_acc: 0.8275\n",
      "Epoch 48/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0045 - acc: 0.9988 - val_loss: 1.0688 - val_acc: 0.8299\n",
      "Epoch 49/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0046 - acc: 0.9987 - val_loss: 1.1094 - val_acc: 0.8347\n",
      "Epoch 50/60\n",
      "6680/6680 [==============================] - 1s 119us/step - loss: 0.0043 - acc: 0.9988 - val_loss: 1.0967 - val_acc: 0.8335\n",
      "Epoch 51/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0048 - acc: 0.9988 - val_loss: 1.0976 - val_acc: 0.8323\n",
      "Epoch 52/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0042 - acc: 0.9990 - val_loss: 1.0977 - val_acc: 0.8323\n",
      "Epoch 53/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0041 - acc: 0.9990 - val_loss: 1.1428 - val_acc: 0.8299\n",
      "Epoch 54/60\n",
      "6680/6680 [==============================] - 1s 120us/step - loss: 0.0031 - acc: 0.9990 - val_loss: 1.1406 - val_acc: 0.8299\n",
      "Epoch 55/60\n",
      "6680/6680 [==============================] - 1s 108us/step - loss: 0.0043 - acc: 0.9984 - val_loss: 1.1262 - val_acc: 0.8371\n",
      "Epoch 56/60\n",
      "6680/6680 [==============================] - 1s 116us/step - loss: 0.0044 - acc: 0.9991 - val_loss: 1.1411 - val_acc: 0.8263\n",
      "Epoch 57/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0043 - acc: 0.9985 - val_loss: 1.1079 - val_acc: 0.8371\n",
      "Epoch 58/60\n",
      "6680/6680 [==============================] - 1s 106us/step - loss: 0.0043 - acc: 0.9985 - val_loss: 1.1244 - val_acc: 0.8311\n",
      "Epoch 59/60\n",
      "6680/6680 [==============================] - 1s 107us/step - loss: 0.0050 - acc: 0.9988 - val_loss: 1.1187 - val_acc: 0.8323\n",
      "Epoch 60/60\n",
      "6680/6680 [==============================] - 1s 112us/step - loss: 0.0045 - acc: 0.9985 - val_loss: 1.1091 - val_acc: 0.8275\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b7e3fd048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 81.9378%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x7f8b7e307eb8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogResnet50Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.Resnet50.hdf5'\n",
    "Resnet50_GAP_model = add_GAP_layer(filepath_bottleneck)\n",
    "finetune_model(Resnet50_GAP_model, filepath_bottleneck, filepath_model, batch_size=50, epochs=60, plot_loss=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also wanted to test another type of layer to see if this would perform better than the basic Global Average Pooling approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_FC_layer(filepath_bottleneck, num_categories=133):\n",
    "    \"\"\"\n",
    "    Build layers on top of a CNN model\n",
    "    \"\"\"\n",
    "    from keras.layers import Dropout, Flatten, Dense\n",
    "    from keras.layers import GlobalAveragePooling2D\n",
    "    from keras.models import Sequential\n",
    "    import numpy as np\n",
    "    \n",
    "    # 1. get input shape\n",
    "    bottleneck_features = np.load(filepath_bottleneck)\n",
    "    train = bottleneck_features['train']\n",
    "    \n",
    "    # 2. add layers to model\n",
    "    model = Sequential()\n",
    "    model.add(Flatten(input_shape=train.shape[1:]))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(num_categories, activation='softmax'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/60\n",
      "6680/6680 [==============================] - 1s 187us/step - loss: 3.1753 - acc: 0.2847 - val_loss: 1.3752 - val_acc: 0.6347\n",
      "Epoch 2/60\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 1.4082 - acc: 0.6042 - val_loss: 0.8322 - val_acc: 0.7449\n",
      "Epoch 3/60\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.9824 - acc: 0.7028 - val_loss: 0.7160 - val_acc: 0.7760\n",
      "Epoch 4/60\n",
      "6680/6680 [==============================] - 1s 142us/step - loss: 0.7518 - acc: 0.7660 - val_loss: 0.6399 - val_acc: 0.7904\n",
      "Epoch 5/60\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.6326 - acc: 0.7970 - val_loss: 0.6532 - val_acc: 0.8012\n",
      "Epoch 6/60\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 0.5424 - acc: 0.8260 - val_loss: 0.6416 - val_acc: 0.8060\n",
      "Epoch 7/60\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 0.4805 - acc: 0.8425 - val_loss: 0.6170 - val_acc: 0.8048\n",
      "Epoch 8/60\n",
      "6680/6680 [==============================] - 1s 148us/step - loss: 0.4252 - acc: 0.8585 - val_loss: 0.6604 - val_acc: 0.8036\n",
      "Epoch 9/60\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 0.3838 - acc: 0.8725 - val_loss: 0.6086 - val_acc: 0.8072\n",
      "Epoch 10/60\n",
      "6680/6680 [==============================] - 1s 139us/step - loss: 0.3391 - acc: 0.8864 - val_loss: 0.6278 - val_acc: 0.8108\n",
      "Epoch 11/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.2940 - acc: 0.8997 - val_loss: 0.6165 - val_acc: 0.8299\n",
      "Epoch 12/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.2772 - acc: 0.9070 - val_loss: 0.6118 - val_acc: 0.8275\n",
      "Epoch 13/60\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.2608 - acc: 0.9129 - val_loss: 0.6026 - val_acc: 0.8204\n",
      "Epoch 14/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.2408 - acc: 0.9172 - val_loss: 0.6304 - val_acc: 0.8216\n",
      "Epoch 15/60\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 0.2238 - acc: 0.9219 - val_loss: 0.7111 - val_acc: 0.8168\n",
      "Epoch 16/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.2259 - acc: 0.9256 - val_loss: 0.6772 - val_acc: 0.8216\n",
      "Epoch 17/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.2015 - acc: 0.9304 - val_loss: 0.7030 - val_acc: 0.8335\n",
      "Epoch 18/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1921 - acc: 0.9347 - val_loss: 0.7092 - val_acc: 0.8287\n",
      "Epoch 19/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1844 - acc: 0.9386 - val_loss: 0.6669 - val_acc: 0.8311\n",
      "Epoch 20/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1794 - acc: 0.9352 - val_loss: 0.6978 - val_acc: 0.8240\n",
      "Epoch 21/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1757 - acc: 0.9430 - val_loss: 0.7335 - val_acc: 0.8371\n",
      "Epoch 22/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1766 - acc: 0.9407 - val_loss: 0.7101 - val_acc: 0.8204\n",
      "Epoch 23/60\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 0.1541 - acc: 0.9457 - val_loss: 0.7624 - val_acc: 0.8347\n",
      "Epoch 24/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1435 - acc: 0.9510 - val_loss: 0.7887 - val_acc: 0.8240\n",
      "Epoch 25/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1423 - acc: 0.9524 - val_loss: 0.7283 - val_acc: 0.8335\n",
      "Epoch 26/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1413 - acc: 0.9516 - val_loss: 0.7620 - val_acc: 0.8192\n",
      "Epoch 27/60\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 0.1405 - acc: 0.9558 - val_loss: 0.7475 - val_acc: 0.8263\n",
      "Epoch 28/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1297 - acc: 0.9546 - val_loss: 0.7496 - val_acc: 0.8455\n",
      "Epoch 29/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1346 - acc: 0.9566 - val_loss: 0.7968 - val_acc: 0.8383\n",
      "Epoch 30/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1355 - acc: 0.9543 - val_loss: 0.8730 - val_acc: 0.8251\n",
      "Epoch 31/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1108 - acc: 0.9635 - val_loss: 0.8328 - val_acc: 0.8251\n",
      "Epoch 32/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1172 - acc: 0.9599 - val_loss: 0.8060 - val_acc: 0.8371\n",
      "Epoch 33/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1177 - acc: 0.9606 - val_loss: 0.7844 - val_acc: 0.8371\n",
      "Epoch 34/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1139 - acc: 0.9603 - val_loss: 0.7377 - val_acc: 0.8539\n",
      "Epoch 35/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1247 - acc: 0.9596 - val_loss: 0.8905 - val_acc: 0.8251\n",
      "Epoch 36/60\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 0.1119 - acc: 0.9630 - val_loss: 0.8687 - val_acc: 0.8359\n",
      "Epoch 37/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.1174 - acc: 0.9632 - val_loss: 0.8251 - val_acc: 0.8311\n",
      "Epoch 38/60\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.1096 - acc: 0.9648 - val_loss: 0.8308 - val_acc: 0.8371\n",
      "Epoch 39/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.1114 - acc: 0.9641 - val_loss: 0.8203 - val_acc: 0.8455\n",
      "Epoch 40/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.1121 - acc: 0.9650 - val_loss: 0.8954 - val_acc: 0.8455\n",
      "Epoch 41/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1111 - acc: 0.9657 - val_loss: 0.8849 - val_acc: 0.8419\n",
      "Epoch 42/60\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.0978 - acc: 0.9686 - val_loss: 0.9167 - val_acc: 0.8443\n",
      "Epoch 43/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.1107 - acc: 0.9663 - val_loss: 0.9293 - val_acc: 0.8275\n",
      "Epoch 44/60\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 0.0939 - acc: 0.9708 - val_loss: 0.9914 - val_acc: 0.8240\n",
      "Epoch 45/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.1042 - acc: 0.9672 - val_loss: 0.8911 - val_acc: 0.8383\n",
      "Epoch 46/60\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 0.1038 - acc: 0.9690 - val_loss: 0.9580 - val_acc: 0.8299\n",
      "Epoch 47/60\n",
      "6680/6680 [==============================] - 1s 145us/step - loss: 0.0972 - acc: 0.9705 - val_loss: 1.0141 - val_acc: 0.8383\n",
      "Epoch 48/60\n",
      "6680/6680 [==============================] - 1s 149us/step - loss: 0.1053 - acc: 0.9672 - val_loss: 1.0220 - val_acc: 0.8263\n",
      "Epoch 49/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.0893 - acc: 0.9728 - val_loss: 0.9698 - val_acc: 0.8323\n",
      "Epoch 50/60\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.0872 - acc: 0.9740 - val_loss: 0.9892 - val_acc: 0.8359\n",
      "Epoch 51/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.0947 - acc: 0.9719 - val_loss: 0.9771 - val_acc: 0.8359\n",
      "Epoch 52/60\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.0946 - acc: 0.9701 - val_loss: 1.0025 - val_acc: 0.8395\n",
      "Epoch 53/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1046 - acc: 0.9704 - val_loss: 0.9835 - val_acc: 0.8287\n",
      "Epoch 54/60\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.0851 - acc: 0.9722 - val_loss: 1.0447 - val_acc: 0.8144\n",
      "Epoch 55/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.0926 - acc: 0.9719 - val_loss: 0.9799 - val_acc: 0.8240\n",
      "Epoch 56/60\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 0.0906 - acc: 0.9713 - val_loss: 0.9607 - val_acc: 0.8299\n",
      "Epoch 57/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.0867 - acc: 0.9720 - val_loss: 1.0520 - val_acc: 0.8383\n",
      "Epoch 58/60\n",
      "6680/6680 [==============================] - 1s 134us/step - loss: 0.0846 - acc: 0.9719 - val_loss: 1.0234 - val_acc: 0.8491\n",
      "Epoch 59/60\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.0776 - acc: 0.9749 - val_loss: 1.0011 - val_acc: 0.8407\n",
      "Epoch 60/60\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.1073 - acc: 0.9693 - val_loss: 1.0179 - val_acc: 0.8335\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b7e6ca780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 84.4498%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x7f8b7e481b38>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogResnet50Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.Resnet50.hdf5'\n",
    "Resnet50_FC_model = add_FC_layer(filepath_bottleneck)\n",
    "finetune_model(Resnet50_FC_model, filepath_bottleneck, filepath_model, batch_size=50, epochs=60, plot_loss=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the fully connected layer (FC layer) did slightly better in terms of accuracy, however, it seems that the validation loss for the FC layer was not converging to a stable loss value. Let's try and improve the GAP model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_GAP_1_layer(filepath_bottleneck, num_categories=133):\n",
    "    \"\"\"\n",
    "    Build layers on top of a CNN model\n",
    "    \n",
    "    Inputs:\n",
    "        filepath_bottleneck - filepath where the bottleneck file is saved\n",
    "        num_categories = number of categories to fit into the CNN\n",
    "    \n",
    "    Return:\n",
    "        model - built Keras CNN model\n",
    "    \n",
    "    \"\"\"\n",
    "    from keras.layers import Dense, Dropout, GlobalAveragePooling2D\n",
    "    from keras.models import Sequential\n",
    "    import numpy as np\n",
    "    \n",
    "    # 1. get input shape\n",
    "    bottleneck_features = np.load(filepath_bottleneck)\n",
    "    train = bottleneck_features['train']\n",
    "    \n",
    "    # 2. add layers to model\n",
    "    model = Sequential()\n",
    "    model.add(GlobalAveragePooling2D(input_shape=train.shape[1:]))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(num_categories, activation='softmax'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/90\n",
      "6680/6680 [==============================] - 1s 199us/step - loss: 4.0121 - acc: 0.1484 - val_loss: 1.9581 - val_acc: 0.5689\n",
      "Epoch 2/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 2.1805 - acc: 0.4299 - val_loss: 1.0161 - val_acc: 0.7198\n",
      "Epoch 3/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 1.5872 - acc: 0.5500 - val_loss: 0.8385 - val_acc: 0.7413\n",
      "Epoch 4/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 1.2893 - acc: 0.6175 - val_loss: 0.7484 - val_acc: 0.7784\n",
      "Epoch 5/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 1.1636 - acc: 0.6512 - val_loss: 0.6792 - val_acc: 0.7952\n",
      "Epoch 6/90\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 1.0127 - acc: 0.6948 - val_loss: 0.6355 - val_acc: 0.7988\n",
      "Epoch 7/90\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.9427 - acc: 0.7133 - val_loss: 0.6249 - val_acc: 0.8084\n",
      "Epoch 8/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.8544 - acc: 0.7353 - val_loss: 0.6431 - val_acc: 0.7988\n",
      "Epoch 9/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.8295 - acc: 0.7488 - val_loss: 0.6092 - val_acc: 0.8156\n",
      "Epoch 10/90\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.7895 - acc: 0.7591 - val_loss: 0.5864 - val_acc: 0.8108\n",
      "Epoch 11/90\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 0.7527 - acc: 0.7693 - val_loss: 0.6394 - val_acc: 0.8036\n",
      "Epoch 12/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.7220 - acc: 0.7783 - val_loss: 0.5820 - val_acc: 0.8299\n",
      "Epoch 13/90\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.7047 - acc: 0.7814 - val_loss: 0.5702 - val_acc: 0.8156\n",
      "Epoch 14/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.6797 - acc: 0.7937 - val_loss: 0.6101 - val_acc: 0.8180\n",
      "Epoch 15/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.6559 - acc: 0.7993 - val_loss: 0.6027 - val_acc: 0.8204\n",
      "Epoch 16/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.6715 - acc: 0.7954 - val_loss: 0.5846 - val_acc: 0.8335\n",
      "Epoch 17/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.6415 - acc: 0.8033 - val_loss: 0.6063 - val_acc: 0.8204\n",
      "Epoch 18/90\n",
      "6680/6680 [==============================] - 1s 127us/step - loss: 0.6204 - acc: 0.8115 - val_loss: 0.6306 - val_acc: 0.8048\n",
      "Epoch 19/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.6094 - acc: 0.8144 - val_loss: 0.6417 - val_acc: 0.8156\n",
      "Epoch 20/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.6253 - acc: 0.8114 - val_loss: 0.6278 - val_acc: 0.8192\n",
      "Epoch 21/90\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 0.5896 - acc: 0.8195 - val_loss: 0.6054 - val_acc: 0.8323\n",
      "Epoch 22/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5653 - acc: 0.8229 - val_loss: 0.6402 - val_acc: 0.8311\n",
      "Epoch 23/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.6056 - acc: 0.8168 - val_loss: 0.6380 - val_acc: 0.8335\n",
      "Epoch 24/90\n",
      "6680/6680 [==============================] - 1s 127us/step - loss: 0.5725 - acc: 0.8207 - val_loss: 0.6261 - val_acc: 0.8120\n",
      "Epoch 25/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.6006 - acc: 0.8238 - val_loss: 0.6296 - val_acc: 0.8251\n",
      "Epoch 26/90\n",
      "6680/6680 [==============================] - 1s 144us/step - loss: 0.5575 - acc: 0.8314 - val_loss: 0.6559 - val_acc: 0.8144\n",
      "Epoch 27/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5588 - acc: 0.8367 - val_loss: 0.6205 - val_acc: 0.8323\n",
      "Epoch 28/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5585 - acc: 0.8319 - val_loss: 0.6247 - val_acc: 0.8024\n",
      "Epoch 29/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5283 - acc: 0.8443 - val_loss: 0.6734 - val_acc: 0.8335\n",
      "Epoch 30/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5429 - acc: 0.8434 - val_loss: 0.6625 - val_acc: 0.8204\n",
      "Epoch 31/90\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 0.5597 - acc: 0.8368 - val_loss: 0.6607 - val_acc: 0.8228\n",
      "Epoch 32/90\n",
      "6680/6680 [==============================] - 1s 132us/step - loss: 0.5356 - acc: 0.8401 - val_loss: 0.6361 - val_acc: 0.8287\n",
      "Epoch 33/90\n",
      "6680/6680 [==============================] - 1s 140us/step - loss: 0.5065 - acc: 0.8515 - val_loss: 0.6622 - val_acc: 0.8299\n",
      "Epoch 34/90\n",
      "6680/6680 [==============================] - 1s 135us/step - loss: 0.4981 - acc: 0.8478 - val_loss: 0.6338 - val_acc: 0.8132\n",
      "Epoch 35/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5225 - acc: 0.8488 - val_loss: 0.6686 - val_acc: 0.8287\n",
      "Epoch 36/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5314 - acc: 0.8494 - val_loss: 0.6806 - val_acc: 0.8263\n",
      "Epoch 37/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5095 - acc: 0.8539 - val_loss: 0.6702 - val_acc: 0.8275\n",
      "Epoch 38/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5442 - acc: 0.8496 - val_loss: 0.7089 - val_acc: 0.8228\n",
      "Epoch 39/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5214 - acc: 0.8496 - val_loss: 0.6753 - val_acc: 0.8251\n",
      "Epoch 40/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5138 - acc: 0.8546 - val_loss: 0.6621 - val_acc: 0.8228\n",
      "Epoch 41/90\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.5041 - acc: 0.8560 - val_loss: 0.6537 - val_acc: 0.8216\n",
      "Epoch 42/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5040 - acc: 0.8536 - val_loss: 0.6942 - val_acc: 0.8347\n",
      "Epoch 43/90\n",
      "6680/6680 [==============================] - 1s 127us/step - loss: 0.4894 - acc: 0.8602 - val_loss: 0.6646 - val_acc: 0.8395\n",
      "Epoch 44/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4839 - acc: 0.8633 - val_loss: 0.6983 - val_acc: 0.8335\n",
      "Epoch 45/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5176 - acc: 0.8627 - val_loss: 0.6766 - val_acc: 0.8168\n",
      "Epoch 46/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5107 - acc: 0.8567 - val_loss: 0.6314 - val_acc: 0.8335\n",
      "Epoch 47/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4816 - acc: 0.8681 - val_loss: 0.7025 - val_acc: 0.8311\n",
      "Epoch 48/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4887 - acc: 0.8638 - val_loss: 0.7244 - val_acc: 0.8180\n",
      "Epoch 49/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.5341 - acc: 0.8575 - val_loss: 0.7131 - val_acc: 0.8335\n",
      "Epoch 50/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5201 - acc: 0.8555 - val_loss: 0.6794 - val_acc: 0.8311\n",
      "Epoch 51/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.4918 - acc: 0.8611 - val_loss: 0.7650 - val_acc: 0.8323\n",
      "Epoch 52/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5074 - acc: 0.8636 - val_loss: 0.7183 - val_acc: 0.8311\n",
      "Epoch 53/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4968 - acc: 0.8623 - val_loss: 0.7167 - val_acc: 0.8275\n",
      "Epoch 54/90\n",
      "6680/6680 [==============================] - 1s 136us/step - loss: 0.4693 - acc: 0.8645 - val_loss: 0.7470 - val_acc: 0.8240\n",
      "Epoch 55/90\n",
      "6680/6680 [==============================] - 1s 142us/step - loss: 0.5270 - acc: 0.8603 - val_loss: 0.7311 - val_acc: 0.8263\n",
      "Epoch 56/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5001 - acc: 0.8657 - val_loss: 0.7111 - val_acc: 0.8287\n",
      "Epoch 57/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4984 - acc: 0.8690 - val_loss: 0.7333 - val_acc: 0.8323\n",
      "Epoch 58/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5169 - acc: 0.8597 - val_loss: 0.7992 - val_acc: 0.8275\n",
      "Epoch 59/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4971 - acc: 0.8674 - val_loss: 0.7277 - val_acc: 0.8407\n",
      "Epoch 60/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.5049 - acc: 0.8681 - val_loss: 0.7246 - val_acc: 0.8216\n",
      "Epoch 61/90\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 0.5014 - acc: 0.8705 - val_loss: 0.7454 - val_acc: 0.8407\n",
      "Epoch 62/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4757 - acc: 0.8702 - val_loss: 0.7898 - val_acc: 0.8275\n",
      "Epoch 63/90\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 0.4894 - acc: 0.8651 - val_loss: 0.7638 - val_acc: 0.8347\n",
      "Epoch 64/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4666 - acc: 0.8714 - val_loss: 0.7787 - val_acc: 0.8347\n",
      "Epoch 65/90\n",
      "6680/6680 [==============================] - 1s 127us/step - loss: 0.4984 - acc: 0.8684 - val_loss: 0.7582 - val_acc: 0.8347\n",
      "Epoch 66/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4965 - acc: 0.8684 - val_loss: 0.7375 - val_acc: 0.8323\n",
      "Epoch 67/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5001 - acc: 0.8725 - val_loss: 0.7659 - val_acc: 0.8275\n",
      "Epoch 68/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4876 - acc: 0.8749 - val_loss: 0.7658 - val_acc: 0.8216\n",
      "Epoch 69/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4980 - acc: 0.8692 - val_loss: 0.7943 - val_acc: 0.8192\n",
      "Epoch 70/90\n",
      "6680/6680 [==============================] - 1s 143us/step - loss: 0.5108 - acc: 0.8678 - val_loss: 0.7348 - val_acc: 0.8311\n",
      "Epoch 71/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4768 - acc: 0.8743 - val_loss: 0.7785 - val_acc: 0.8323\n",
      "Epoch 72/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4781 - acc: 0.8784 - val_loss: 0.8087 - val_acc: 0.8263\n",
      "Epoch 73/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5034 - acc: 0.8754 - val_loss: 0.7935 - val_acc: 0.8299\n",
      "Epoch 74/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4921 - acc: 0.8750 - val_loss: 0.8064 - val_acc: 0.8263\n",
      "Epoch 75/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4963 - acc: 0.8753 - val_loss: 0.7741 - val_acc: 0.8228\n",
      "Epoch 76/90\n",
      "6680/6680 [==============================] - 1s 138us/step - loss: 0.4937 - acc: 0.8754 - val_loss: 0.7839 - val_acc: 0.8251\n",
      "Epoch 77/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5039 - acc: 0.8714 - val_loss: 0.8377 - val_acc: 0.8251\n",
      "Epoch 78/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4771 - acc: 0.8775 - val_loss: 0.7907 - val_acc: 0.8323\n",
      "Epoch 79/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5012 - acc: 0.8775 - val_loss: 0.7933 - val_acc: 0.8204\n",
      "Epoch 80/90\n",
      "6680/6680 [==============================] - 1s 128us/step - loss: 0.4845 - acc: 0.8805 - val_loss: 0.8228 - val_acc: 0.8192\n",
      "Epoch 81/90\n",
      "6680/6680 [==============================] - 1s 141us/step - loss: 0.4907 - acc: 0.8787 - val_loss: 0.8451 - val_acc: 0.8275\n",
      "Epoch 82/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5188 - acc: 0.8768 - val_loss: 0.8078 - val_acc: 0.8263\n",
      "Epoch 83/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4911 - acc: 0.8753 - val_loss: 0.8449 - val_acc: 0.8240\n",
      "Epoch 84/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4983 - acc: 0.8723 - val_loss: 0.8387 - val_acc: 0.8395\n",
      "Epoch 85/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.4987 - acc: 0.8808 - val_loss: 0.8432 - val_acc: 0.8287\n",
      "Epoch 86/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.4908 - acc: 0.8801 - val_loss: 0.8072 - val_acc: 0.8383\n",
      "Epoch 87/90\n",
      "6680/6680 [==============================] - 1s 129us/step - loss: 0.5117 - acc: 0.8786 - val_loss: 0.8609 - val_acc: 0.8156\n",
      "Epoch 88/90\n",
      "6680/6680 [==============================] - 1s 130us/step - loss: 0.5026 - acc: 0.8783 - val_loss: 0.9208 - val_acc: 0.8216\n",
      "Epoch 89/90\n",
      "6680/6680 [==============================] - 1s 131us/step - loss: 0.5120 - acc: 0.8765 - val_loss: 0.8058 - val_acc: 0.8275\n",
      "Epoch 90/90\n",
      "6680/6680 [==============================] - 1s 133us/step - loss: 0.5140 - acc: 0.8790 - val_loss: 0.8744 - val_acc: 0.8323\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b8408eba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 81.6986%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x7f8b7e7a60f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogResnet50Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.Resnet50.hdf5'\n",
    "Resnet50_GAP_1_model = add_GAP_1_layer(filepath_bottleneck)\n",
    "finetune_model(Resnet50_GAP_1_model, filepath_bottleneck, filepath_model, batch_size=50, epochs=90, plot_loss=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Predict Dog Breed with the Model\n",
    "\n",
    "Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan_hound`, etc) that is predicted by your model.  \n",
    "\n",
    "Similar to the analogous function in Step 5, your function should have three steps:\n",
    "1. Extract the bottleneck features corresponding to the chosen CNN model.\n",
    "2. Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.\n",
    "3. Use the `dog_names` array defined in Step 0 of this notebook to return the corresponding breed.\n",
    "\n",
    "The functions to extract the bottleneck features can be found in `extract_bottleneck_features.py`, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function\n",
    "\n",
    "    extract_{network}\n",
    "    \n",
    "where `{network}`, in the above filename, should be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_bottleneck = 'bottleneck_features/DogResnet50Data.npz'\n",
    "filepath_model = 'saved_models/weights.best.Resnet50.hdf5'\n",
    "Resnet50_model = add_GAP_1_layer(filepath_bottleneck)\n",
    "Resnet50_model.load_weights(filepath_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: Write a function that takes a path to an image as input\n",
    "### and returns the dog breed that is predicted by the model.\n",
    "from extract_bottleneck_features import *\n",
    "\n",
    "def Resnet50_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))\n",
    "    # obtain predicted vector\n",
    "    predicted_vector = Resnet50_model.predict(bottleneck_feature)\n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step6'></a>\n",
    "## Step 6: Write your Algorithm\n",
    "\n",
    "Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,\n",
    "- if a __dog__ is detected in the image, return the predicted breed.\n",
    "- if a __human__ is detected in the image, return the resembling dog breed.\n",
    "- if __neither__ is detected in the image, provide output that indicates an error.\n",
    "\n",
    "You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `dog_detector` functions developed above.  You are __required__ to use your CNN from Step 5 to predict dog breed.  \n",
    "\n",
    "A sample image and output for our algorithm is provided below, but feel free to design your own user experience!\n",
    "\n",
    "![Sample Human Output](images/sample_human_2.png)\n",
    "\n",
    "This photo looks like an Afghan Hound.\n",
    "### (IMPLEMENTATION) Write your Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: Write your algorithm.\n",
    "### Feel free to use as many code cells as needed.\n",
    "def which_dog_breed_are_you(img_path):\n",
    "    import re\n",
    "    import cv2                \n",
    "    import matplotlib.pyplot as plt                        \n",
    "    %matplotlib inline                               \n",
    "\n",
    "    # load and display image\n",
    "    img = cv2.imread(img_path)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    \n",
    "    # run through algorithm\n",
    "    pattern = re.compile(r'[A-Za-z_]+$')\n",
    "    if dog_detector(img_path):\n",
    "        print(\"Hello, dog!\")\n",
    "        print(\"Your predicted breed is...\")\n",
    "        print(re.sub('_', ' ', pattern.findall(Resnet50_predict_breed(img_path))[0]))\n",
    "    elif face_detector(img_path):\n",
    "        print(\"Hello, human!\")\n",
    "        print(\"Your predicted breed is...\")\n",
    "        print(re.sub('_', ' ', pattern.findall(Resnet50_predict_breed(img_path))[0]))\n",
    "    else:\n",
    "        raise Exception(\n",
    "            'The picture uploaded is neither a dog nor a human. Please try uploading another photo.'\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "## Step 7: Test Your Algorithm\n",
    "\n",
    "In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that __you__ look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?\n",
    "\n",
    "### (IMPLEMENTATION) Test Your Algorithm on Sample Images!\n",
    "\n",
    "Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.  \n",
    "\n",
    "__Question 6:__ Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bcaa88710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, dog!\n",
      "Your predicted breed is...\n",
      "Icelandic sheepdog\n"
     ]
    }
   ],
   "source": [
    "## TODO: Execute your algorithm from Step 6 on\n",
    "## at least 6 images on your computer.\n",
    "## Feel free to use as many code cells as needed.\n",
    "which_dog_breed_are_you('images/dude.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bf4191630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, dog!\n",
      "Your predicted breed is...\n",
      "Silky terrier\n"
     ]
    }
   ],
   "source": [
    "which_dog_breed_are_you('images/hachi.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c9d3506a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, human!\n",
      "Your predicted breed is...\n",
      "American staffordshire terrier\n"
     ]
    }
   ],
   "source": [
    "which_dog_breed_are_you('images/kn.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a82c9de80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, dog!\n",
      "Your predicted breed is...\n",
      "Portuguese water dog\n"
     ]
    }
   ],
   "source": [
    "which_dog_breed_are_you('images/lucky.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a80111470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, human!\n",
      "Your predicted breed is...\n",
      "Basenji\n"
     ]
    }
   ],
   "source": [
    "which_dog_breed_are_you('images/sebastian.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bcaa88668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, human!\n",
      "Your predicted breed is...\n",
      "Chihuahua\n"
     ]
    }
   ],
   "source": [
    "which_dog_breed_are_you('images/tofu.jpeg')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
